Python (12) ์ธ๋ค์ผํ ๋ฆฌ์คํธํ [Python]Pandas _ Merge, Concat โ Data Load # Data Load (df1, df2) DataUrl = 'https://raw.githubusercontent.com/Datamanim/pandas/main/mergeTEst.csv' df = pd.read_csv(DataUrl) df = df.set_index('Location') df1 = df.iloc[:4,:] df2 = df.iloc[4:,:] display(df1) display(df2) โ df1, df2 ๋ฐ์ดํฐ๋ฅผ ํ๋์ ๋ฐ์ดํฐ ํ๋ ์์ผ๋ก ํฉ์น๊ธฐ # df1, df2 ๋ฐ์ดํฐ๋ฅผ ํ๋์ ๋ฐ์ดํฐ ํ๋ ์์ผ๋ก ํฉ์น๊ธฐ total = pd.concat([df1,df2]) Ans = total Ans โ df3๊ณผ df4 ๋ฐ์ดํฐ๋ฅผ ํ๋์ ๋ฐ์ดํฐ ํ๋ ์์ผ๋ก ํฉ์น๊ธฐ # df3๊ณผ df4 ๋ฐ.. [Python] Pandas _ Pivot โ Data Load & ์ปฌ๋ผ ์ญ์ ๋ฐ ์์ # Data Load # Indicator์ ์ญ์ ํ๊ณ First Tooltip ์ปฌ๋ผ์์ ์ ๋ขฐ๊ตฌ๊ฐ์ ํด๋นํ๋ ํํ์ ์ง์ฐ๊ธฐ DataUrl = 'https://raw.githubusercontent.com/Datamanim/pandas/main/under5MortalityRate.csv' df = pd.read_csv(DataUrl) df.drop('Indicator', axis=1, inplace=True) df['First Tooltip'] = df['First Tooltip'].map(lambda x : float(x.split("[")[0])) Ans = df Ans.head(4) โ ํน์ ์ผ์ด์ค ์ถ์ถ # ๋ ๋๊ฐ 2015๋ ์ด์, Dim1์ด Both sex.. [Python] Pandas _ Time Series โ Data Load # Data Load / Data type ํ์ DataUrl = 'https://raw.githubusercontent.com/Datamanim/pandas/main/timeTest.csv' df = pd.read_csv(DataUrl) Ans = df.info() RangeIndex: 6574 entries, 0 to 6573 Data columns (total 13 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Yr_Mo_Dy 6574 non-null object 1 RPT 6568 non-null float64 2 VAL 6571 non-null float64 3 ROS 6572 non-nu.. [Python] Pandas _ Apply, Map โ Data Load # Data Load & ๋ฐ์ดํฐ ํ, ์ด์ ๊ฐฏ์ ์ถ๋ ฅ DataUrl = 'https://raw.githubusercontent.com/Datamanim/pandas/main/BankChurnersUp.csv' df = pd.read_csv(DataUrl) Ans = df.shape Ans (10127, 19) โ Mapping # Income_Category์ ์นดํ ๊ณ ๋ฆฌ๋ฅผ map ํจ์๋ฅผ ์ด์ฉํ์ฌ ๋ค์๊ณผ ๊ฐ์ด ๋ณ๊ฒฝํ์ฌ newIncome ์ปฌ๋ผ์ ๋งคํํ๋ผ # Unknown : N # Less than $40K : a # $40K - $60K : b # $60K - $80K : c # $80K - $120K : d # $120K + : e dic = { 'Unknown' : 'N', 'Les.. [Python] Pandas data ์ฒ๋ฆฌ ๐ ๋๋ฝ ๋ฐ์ดํฐ ํ์ธ import seaborn as sns df = sns.load_dataset('titanic') df.head() df.info() nan_deck = df['deck'].value_counts(dropna=False) print(nan_deck) ๐ 'deck' ์ด์ NaN ๊ฐ์ด ์์์ ํ์ธ / 'deck' ์ด์ ์๋ ๋๋ฝ ๋ฐ์ดํฐ๊ฐ 688๊ฐ ๋ผ๋ ์ฌ์ค์ ์ ์ ์์ ๐ ๋๋ฝ ๋ฐ์ดํฐ ์ ๊ฑฐ import seaborn as sns df = sns.load_dataset('titanic') missing_df = df.isnull() for col in missing_df.columns: missing_count = missing_df[col].value_counts() try: pri.. [Python] Pandas data: auto-mpg data ์๊ฐํ ๐ mpg data ๋ถ๋ฌ์ค๊ธฐ # -*- coding: utf-8 -*- import pandas as pd df = pd.read_csv('./auto-mpg.csv',header=None) # df print(df.head()) 0 1 2 3 4 5 6 \ 0 mpg cylinders displacement horsepower weight acceleration model year 1 18 8 307 130 3504 12 70 2 15 8 350 165 3693 11.5 70 3 18 8 318 150 3436 11 70 4 16 8 304 150 3433 12 70 7 8 0 origin car name 1 1 chevrolet chevelle malibu 2 1 buick skylark 320 3 .. [Python] matplotlib, seaborn ๋ง๋๊ทธ๋ํ ๊ทธ๋ฆฌ๊ธฐ / ๊พธ๋ฏธ๊ธฐ ๐ ๊ตฌ๊ธ ๋๋ผ์ด๋ธ - colab ์ฐ๋ from google.colab import drive drive.mount('/content/drive') ๐จ tips data ๋ถ๋ฌ์ค๊ธฐ import matplotlib.pyplot as plt import seaborn as sns import numpy as np tips = sns.load_dataset('tips') tips.head() โ ์ถ ํฌ๋งทํ # ์ถ ํฌ๋งทํ (ํจ์ ์ง์ ) from matplotlib.ticker import (MultipleLocator, AutoMinorLocator, FuncFormatter) def major_fomatter(x, pos): return "%.2f+$" % x formatter = FuncFormatter(maj.. [Python] docstring (๋ฌธ์ํ / ์ฌ์ฉ์ ์ ์ ํจ์ ) ๐ python docstring references: https://wikidocs.net/16050 24. Docstring - ๋ฌธ์ํ - ํ์ด์ฌ์ ์ ํ๋ฉด์, ์ฌ๋ฌ๊ฐ์ง๋ก ๋ฉ์ง๋ค๋ผ๊ณ ์๊ฐํ๋ ๊ฒ ์ค์ ํ๋ - docstring์ ์์ฑํ๋ฉด, ํ๋ก๊ทธ๋๋ฐ์ ์์ฑ์ผ๋ก ์ ๊ทผํ ์ ์์. ## 1. docstring ์ด๋ … wikidocs.net docstring์ ๋ชจ๋, ํจ์, ํด๋์ค ๋๋ ๋ฉ์๋ ์ ์์ ์ฒซ ๋ฒ์งธ ๋ช ๋ น๋ฌธ์ผ๋ก ๋ฐ์ํ๋ ๋ฌธ์์ด ๋ฆฌํฐ๋ด Module ์ฒซ๋ฒ์งธ ์ค, ํจ์ ์ ์ธ ํ ๋ด๋ถ ๋ฐ๋ก ์๋ซ์ค ๋๋ ํด๋์ค ์ ์ธ ํ ๋ด๋ถ ๋ฐ๋ก ์๋ซ์ค์ ํฐ๋ฐ์ดํ 3๊ฐ๋ ์์ ๋ฐ์ดํ 3๊ฐ๋ก ์์ฑํ๋ฉด ๋๋ค. ๐ Docstring ์ฌ์ฉ ์์ def hello(): """์ด ํจ์๋ 'Hello World!'๋ฅผ ์ถ๋ ฅํ๋ค!""" p.. ์ด์ 1 2 ๋ค์